Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions (Blockchain Technologies) - Tapa dura

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Dr. Abhishek Kumar is an Assistant Director and Professor in the Computer Science & Engineering Department at Chandigarh University, Punjab, India. He obtained his Ph.D. from the University of Madras and completed postdoctoral research at Universidad de Castilla-La Mancha, Spain. His research interests span artificial intelligence, renewable energy systems, image processing, and data mining.

Dr. Priya Batta is currently working as an Associate Professor in the Computer Science & Engineering Department in Chandigarh University, Mohali, India. She is Doctorate in Computer Science & Engineering from Chandigarh University. Her research area includes- Artificial intelligence, Blockchain, IoT.

Dr. T. Ananth Kumar is working as a Associate Professor and Research Head at IFET college of Engineering(Autonomous),I ndia. He received his Ph.D. in VLSI Design from Manonmaniam Sundaranar University, Tirunelveli, India. His research interest are in the areas of Networks on Chips, Computer Architecture and ASIC design.

Dr. S. Oswalt Manoj is working as a Professor in the Department of Computer Science and Engineering, Alliance School of Advanced Computing ,Alliance University, Bengaluru, India. He holds a Doctorate in Information Science and Engineering from Anna University, Chennai. His research areas include big data analytics, artificial intelligence, computer vision, machine learning, deep learning, and cloud computing.

De la contraportada

This book highlights the transformative synergy between Blockchain and Federated Learning in developing privacy-focused solutions for DeepFex. By leveraging the decentralized nature of blockchain alongside the privacy-preserving capabilities of federated learning, it offers a novel approach to combating the growing challenges of deepfake technology. The integration of these two cutting-edge technologies ensures data security, model integrity, and transparent collaboration, making it possible to detect and mitigate deepfakes in a scalable, ethical, and decentralized manner.